A small example of this would be NFL / NBA Refs fixing playoff games with a bad call or two. This actually happened 20 years ago, an NBA ref went to prison over being bribed just $2000 per game.

The much worse example is the fact that you can make 100-1 odds on whether the US airstrikes Iran today... or How many times Pam Bondi says the word "China" in a press conference.

So it would be interesting to measure the inefficiencies of various bets vs the total market value in that bet.

e: Although full disclosure, I did not pick apart the entire paper. Maybe it's buried in there.

For example, one of the top trending ~~bets~~ markets right now is on whether Miami or Indiana will win the NCAA football championship tonight. You can either take "Yes" on Indiana at 74c, or "No" at 27c, or you can take "Yes" on Miami at 27c or "No" at 74c. Or, there's another potential outcome - you can also bet on a tie at 10c yes/91c no.

Is this research suggesting that an optimistic Miami fan can somehow get a better return by buying "No" on Indiana than a "Yes" on Miami?

Why is Kalshi structured with these yes vs. no options for all outcomes?

1. The article mentions the bid/ask spread for contracts, but I believe that Kalshi also has its own fee structure. Small edges (an expected loss of 0.57¢ on a 1¢ contract implies an expected gain of 0.43¢ on a 99¢ contract, or a 5.75ppt edge) can be easily eaten by even small fees, and liquidity provision is all about small edges.

2. The article ignores the time value of money, and contracts take time to resolve. If a contract won't resolve for six months and the risk-free rate is 5%, then buying a "sure thing" over 97.5¢ is a loss net of otherwise earnable interest.

3. Long shots offer greater implied leverage to bettors, making them more attractive. This is still (sometimes) an exploitable mispricing, but it's closer to the well-understood "bet against beta" factor.

(Edit to add) Also, I think their explanation of the non-returns on finance is lacking:

> Why is Finance efficient? The likely explanation is participant selection; financial questions attract traders who think in probabilities and expected values rather than fans betting on their favorite team or partisans betting on a preferred candidate. The questions themselves are dry ("Will the S&P close above 6000?"), which filters out emotional bettors.

Financial contracts are the ones that are most perfectly hedges with existing markets. "Will the S&P close above X?" is a binary option, after all, so it's comparatively easy for a market-maker to almost perfectly offset their Kalshi positions with opposite positions in traditional markets.

I get that the finance market is _more_ dry and quantitative than sports, but certainly not immune to hope and tribalism,

And the people losing their life savings on gambling now have one more tool.

But what do I know. I’m probably oblivious to what greatness those Truth Engines will enable.

For instance, if you spot malware in a commit you could bet heavily against it being merged, and that would attract the maintainers' attention, and they'll see what you see and not merge it, and you get paid for the code review--that money would come from whoever bet that it would get merged, which you could require be the author of the malware. I haven't worked it out entirely but it seems that there are opportunities to build games that reward dilligence and transparency and penalize deception and spam.

Could you use inefficient markets as a predictor of great volumes of insider trading?

dataset: 72.1m trades and $18.26b volume on kalshi (2021-2025)

core findings:

longshot bias: well documented longshot bias is present on kalshi. low probability contracts are systematically overpriced. contracts trading at 5 cents only win 4.18% of the time.

wealth transfer: liquidity takers lose money (-1.12% excess return) while liquidity makers earn it (+1.12%).

optimism tax: the losses are driven by a preference for "yes" outcomes. buying "yes" at 1 cent has a -41% expected value. buying "no" at 1 cent has a +23% expected value.

category variation: finance markets are efficient (0.17% maker-taker gap) while high-engagement categories like media and world events are inefficient (>7% gap).

mechanism: makers do not win by out-forecasting takers. they win by passively selling "yes" contracts to optimistic bettors

> Yet on Kalshi, a CFTC-regulated prediction market, traders have wagered vast sums on longshot contracts with historical returns as low as 43 cents on the dollar.

On prediction markets traders can bet both sides. E.g. on Polymarket I can currently bet that Greenland will be acquired by USA before 2027 and get 4:1 odds: or I can bet that this doesn't happen, and give 4:1 odds. If these odds are off, doesn't this mean that one side gets a bad return on investment, however the other side gets an equally good return?

On balance the average return on investment by traders should just be 100 cents minus the margin of the prediction market, which tends to be only a few percent.

In the "The Mechanism of Extraction" section, how is that image made? It is nicely laid out, and has a nice "hand-drawn" feel. This is a good format for many technical drawings, but I have not found any tools that could create this.

In prediction markets if the markets are fully efficiently priced, in the absence of transaction costs you WILL get 100% back in the long run.

Slots are also unskilled games, prediction markets clearly some participants have a clear market edge, thus not efficiently priced.

If the odds in some financial products are worse than gambling while everyone can access gambling, then people should stop making a distinction under the guise of protecting investors

it just drives investors to actual gambling because they cant get the exposure they were already looking for